4 papers
Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation
Shuo Lu, Jianjie Cheng, Yinuo Xu +16
Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the…
How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1
Yinuo Xu, Shuo Lu, Jianjie Cheng +5
Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. While reinforcement learning (RL) has been shown to improve pe…
DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicing
Shuo Lu, Yinuo Xu, Jianjie Cheng +3
Deep Research agents predominantly optimize search policies to maximize retrieval probability. However, we identify a critical bottleneck: the retrieval-utilization gap, where mode…
Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
Yongcan Yu, Lingxiao He, Shuo Lu +10
Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away fro…